Sakshm AI: Advancing AI-Assisted Coding Education for Engineering Students in India Through Socratic Tutoring and Comprehensive Feedback
Raj Gupta, Harshita Goyal, Dhruv Kumar, Apurv Mehra, Sanchit Sharma, Kashish Mittal, Jagat Sesh Challa
TL;DR
Sakshm AI addresses a critical need in AI-assisted coding education by integrating a Socratic tutoring chatbot, Disha, with a large problem bank, an integrated IDE, and detailed performance feedback. The system combines LLM-based code analysis with auxiliary AI models to provide contextual hints, adaptive guidance, and company-specific interview preparation, while maintaining conversational memory across sessions. A large-scale mixed-methods study (n=1170 solved users; 45 survey respondents; 25 interviews) demonstrates that Socratic guidance can enhance critical thinking and engagement, though time-constrained or highly challenging tasks may still benefit from direct answers via other tools. The work offers design recommendations and establishes a framework for scalable, intelligent tutoring that supports Sustainable Development Goal 4 by improving access to quality coding education through adaptive, context-aware AI assistance.
Abstract
The advent of Large Language Models (LLMs) is reshaping education, particularly in programming, by enhancing problem-solving, enabling personalized feedback, and supporting adaptive learning. Existing AI tools for programming education struggle with key challenges, including the lack of Socratic guidance, direct code generation, limited context retention, minimal adaptive feedback, and the need for prompt engineering. To address these challenges, we introduce Sakshm AI, an intelligent tutoring system for learners across all education levels. It fosters Socratic learning through Disha, its inbuilt AI chatbot, which provides context-aware hints, structured feedback, and adaptive guidance while maintaining conversational memory and supporting language flexibility. This study examines 1170 registered participants, analyzing platform logs, engagement trends, and problem-solving behavior to assess Sakshm AI's impact. Additionally, a structured survey with 45 active users and 25 in-depth interviews was conducted, using thematic encoding to extract qualitative insights. Our findings reveal how AI-driven Socratic guidance influences problem-solving behaviors and engagement, offering key recommendations for optimizing AI-based coding platforms. This research combines quantitative and qualitative insights to inform AI-assisted education, providing a framework for scalable, intelligent tutoring systems that improve learning outcomes. Furthermore, Sakshm AI represents a significant step toward Sustainable Development Goal 4 Quality Education, providing an accessible and structured learning tool for undergraduate students, even without expert guidance. This is one of the first large-scale studies examining AI-assisted programming education across multiple institutions and demographics.